Efficient global optimisation of microwave antennas based on a parallel surrogate model-assisted evolutionary algorithm

• Published in: IET microwaves, antennas & propagation Vol. 13; no. 2; pp. 149 - 155
• Main Authors: Liu, Bo, Akinsolu, Mobayode O, Ali, Nazar, Abd-Alhameed, Raed
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 06.02.2019
• Subjects: antenna theory; computational efficiency; dielectric resonator antenna; dielectric resonator antennas; efficient global optimisation; electromagnetic simulations; evolutionary algorithm‐based antenna optimisation methods; evolutionary computation; mathematical benchmark problems; microwave antenna arrays; microwave antennas; normal desktop 4‐core workstation; optimisation efficiency; parallel particle swarm optimisation; parallel surrogate model‐assisted evolutionary algorithm; particle swarm optimisation; PSADEA; Research Article; standard parallel differential evolution algorithm; surrogate model‐assisted EA; Yagi antenna arrays; Yagi‐Uda antenna; efficient global optimisation; microwave antennas; parallel surrogate model-assisted evolutionary algorithm; particle swarm optimisation; evolutionary algorithm-based antenna optimisation methods; optimisation efficiency; computational efficiency; surrogate model-assisted EA; mathematical benchmark problems; Yagi antenna arrays; evolutionary computation; PSADEA; dielectric resonator antenna; Yagi-Uda antenna; antenna theory; microwave antenna arrays; dielectric resonator antennas; parallel particle swarm optimisation; electromagnetic simulations; normal desktop 4-core workstation; standard parallel differential evolution algorithm
• ISSN: 1751-8725; 1751-8733; 1751-8733
• DOI: 10.1049/iet-map.2018.5009

Computational efficiency is a major challenge for evolutionary algorithm (EA)-based antenna optimisation methods due to the computationally expensive electromagnetic simulations. Surrogate model-assisted EAs considerably improve the optimisation efficiency, but most of them are sequential methods, which cannot benefit from parallel simulation of multiple candidate designs for further speed improvement. To address this problem, a new method, called parallel surrogate model-assisted hybrid differential evolution for antenna optimisation (PSADEA), is proposed. The performance of PSADEA is demonstrated by a dielectric resonator antenna, a Yagi-Uda antenna, and three mathematical benchmark problems. Experimental results show high operational performance in a few hours using a normal desktop 4-core workstation. Comparisons show that PSADEA possesses significant advantages in efficiency compared to a state-of-the-art surrogate model-assisted EA for antenna optimisation, the standard parallel differential evolution algorithm, and parallel particle swarm optimisation. In addition, PSADEA also shows stronger optimisation ability compared to the above reference methods for challenging design cases.